Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1015.8 KiB
Average record size in memory104.0 B

Variable types

Numeric12
Categorical1

Alerts

Power (p1) is highly overall correlated with Power (p2) and 2 other fieldsHigh correlation
Power (p2) is highly overall correlated with Power (p1)High correlation
Power (p3) is highly overall correlated with Power (p1)High correlation
Power (p4) is highly overall correlated with Power (p1)High correlation
Reaction Time (tau1) is uniformly distributedUniform
Reaction Time (tau2) is uniformly distributedUniform
Reaction Time (tau3) is uniformly distributedUniform
Reaction Time (tau4) is uniformly distributedUniform
Power (p2) is uniformly distributedUniform
Power (p3) is uniformly distributedUniform
Power (p4) is uniformly distributedUniform
Price Elasticity Coefficient (g1) is uniformly distributedUniform
Price Elasticity Coefficient (g2) is uniformly distributedUniform
Price Elasticity Coefficient (g3) is uniformly distributedUniform
Price Elasticity Coefficient (g4) is uniformly distributedUniform
Reaction Time (tau1) has unique valuesUnique
Reaction Time (tau2) has unique valuesUnique
Reaction Time (tau3) has unique valuesUnique
Reaction Time (tau4) has unique valuesUnique
Power (p1) has unique valuesUnique
Power (p2) has unique valuesUnique
Power (p3) has unique valuesUnique
Power (p4) has unique valuesUnique
Price Elasticity Coefficient (g1) has unique valuesUnique
Price Elasticity Coefficient (g2) has unique valuesUnique
Price Elasticity Coefficient (g3) has unique valuesUnique
Price Elasticity Coefficient (g4) has unique valuesUnique

Reproduction

Analysis started2024-05-05 03:50:00.634043
Analysis finished2024-05-05 03:50:30.632839
Duration30 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Reaction Time (tau1)
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2499999
Minimum0.50079302
Maximum9.9994695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:30.954925image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.50079302
5-th percentile0.97566115
Q12.8748916
median5.2500039
Q37.6246897
95-th percentile9.5245669
Maximum9.9994695
Range9.4986764
Interquartile range (IQR)4.7497981

Descriptive statistics

Standard deviation2.7425484
Coefficient of variation (CV)0.52239017
Kurtosis-1.2000025
Mean5.2499999
Median Absolute Deviation (MAD)2.3749818
Skewness-5.4116433 × 10-6
Sum52499.999
Variance7.5215716
MonotonicityNot monotonic
2024-05-05T10:50:31.324897image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.959060025 1
 
< 0.1%
4.928146725 1
 
< 0.1%
8.94226567 1
 
< 0.1%
9.739026491 1
 
< 0.1%
5.593826003 1
 
< 0.1%
4.745688089 1
 
< 0.1%
1.261257249 1
 
< 0.1%
8.559192065 1
 
< 0.1%
7.581259541 1
 
< 0.1%
6.589616423 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.500793021 1
< 0.1%
0.501330761 1
< 0.1%
0.502320629 1
< 0.1%
0.503283798 1
< 0.1%
0.504014262 1
< 0.1%
0.50496005 1
< 0.1%
0.506418908 1
< 0.1%
0.506735963 1
< 0.1%
0.508533205 1
< 0.1%
0.509123998 1
< 0.1%
ValueCountFrequency (%)
9.999469469 1
< 0.1%
9.998994574 1
< 0.1%
9.997738127 1
< 0.1%
9.99667792 1
< 0.1%
9.995375211 1
< 0.1%
9.99435553 1
< 0.1%
9.993960677 1
< 0.1%
9.99304592 1
< 0.1%
9.991797997 1
< 0.1%
9.991046395 1
< 0.1%

Reaction Time (tau2)
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.250001
Minimum0.50014136
Maximum9.9998366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:31.707898image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.50014136
5-th percentile0.97514613
Q12.87514
median5.2499813
Q37.6248931
95-th percentile9.5243643
Maximum9.9998366
Range9.4996952
Interquartile range (IQR)4.7497531

Descriptive statistics

Standard deviation2.7425487
Coefficient of variation (CV)0.52239012
Kurtosis-1.2000006
Mean5.250001
Median Absolute Deviation (MAD)2.3749342
Skewness2.7402994 × 10-6
Sum52500.01
Variance7.5215732
MonotonicityNot monotonic
2024-05-05T10:50:32.121023image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.079885204 1
 
< 0.1%
5.40194926 1
 
< 0.1%
3.23540975 1
 
< 0.1%
8.486152867 1
 
< 0.1%
8.071695838 1
 
< 0.1%
4.157054315 1
 
< 0.1%
4.792743904 1
 
< 0.1%
8.386520359 1
 
< 0.1%
4.061262896 1
 
< 0.1%
7.43767053 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.50014136 1
< 0.1%
0.501581642 1
< 0.1%
0.502048697 1
< 0.1%
0.503084474 1
< 0.1%
0.504128181 1
< 0.1%
0.50537151 1
< 0.1%
0.505833675 1
< 0.1%
0.507243869 1
< 0.1%
0.507845852 1
< 0.1%
0.50864475 1
< 0.1%
ValueCountFrequency (%)
9.999836556 1
< 0.1%
9.998291236 1
< 0.1%
9.997907131 1
< 0.1%
9.996801988 1
< 0.1%
9.995836373 1
< 0.1%
9.994988422 1
< 0.1%
9.994263437 1
< 0.1%
9.993212845 1
< 0.1%
9.992243578 1
< 0.1%
9.990606318 1
< 0.1%

Reaction Time (tau3)
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2500035
Minimum0.50078815
Maximum9.99945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:32.499107image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.50078815
5-th percentile0.9752198
Q12.8755217
median5.2499786
Q37.6249483
95-th percentile9.5241719
Maximum9.99945
Range9.4986619
Interquartile range (IQR)4.7494266

Descriptive statistics

Standard deviation2.7425495
Coefficient of variation (CV)0.52239002
Kurtosis-1.2000044
Mean5.2500035
Median Absolute Deviation (MAD)2.3749953
Skewness-4.7999599 × 10-6
Sum52500.035
Variance7.5215775
MonotonicityNot monotonic
2024-05-05T10:50:32.842460image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.381025392 1
 
< 0.1%
4.576657507 1
 
< 0.1%
8.137190295 1
 
< 0.1%
7.816201133 1
 
< 0.1%
5.347266008 1
 
< 0.1%
7.855994248 1
 
< 0.1%
1.518336474 1
 
< 0.1%
4.13077177 1
 
< 0.1%
6.192641155 1
 
< 0.1%
1.980074638 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.500788153 1
< 0.1%
0.501657674 1
< 0.1%
0.502376193 1
< 0.1%
0.503481864 1
< 0.1%
0.504430847 1
< 0.1%
0.505143793 1
< 0.1%
0.506481617 1
< 0.1%
0.507573347 1
< 0.1%
0.508035292 1
< 0.1%
0.509371655 1
< 0.1%
ValueCountFrequency (%)
9.999450008 1
< 0.1%
9.998113926 1
< 0.1%
9.997427645 1
< 0.1%
9.996263302 1
< 0.1%
9.995595348 1
< 0.1%
9.994695814 1
< 0.1%
9.994124795 1
< 0.1%
9.992981944 1
< 0.1%
9.992073047 1
< 0.1%
9.991003795 1
< 0.1%

Reaction Time (tau4)
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2499971
Minimum0.50047296
Maximum9.9994433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:33.199571image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.50047296
5-th percentile0.97521913
Q12.8749502
median5.2497341
Q37.6248377
95-th percentile9.5241998
Maximum9.9994433
Range9.4989703
Interquartile range (IQR)4.7498875

Descriptive statistics

Standard deviation2.7425555
Coefficient of variation (CV)0.52239182
Kurtosis-1.1999988
Mean5.2499971
Median Absolute Deviation (MAD)2.375011
Skewness-1.4090435 × 10-6
Sum52499.971
Variance7.5216108
MonotonicityNot monotonic
2024-05-05T10:50:33.566516image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.780754432 1
 
< 0.1%
1.7214366 1
 
< 0.1%
7.045864346 1
 
< 0.1%
9.380778307 1
 
< 0.1%
6.072063152 1
 
< 0.1%
7.478550699 1
 
< 0.1%
4.05517998 1
 
< 0.1%
4.221387264 1
 
< 0.1%
6.557268887 1
 
< 0.1%
3.198011962 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.500472961 1
< 0.1%
0.501571213 1
< 0.1%
0.502024588 1
< 0.1%
0.502878675 1
< 0.1%
0.504015628 1
< 0.1%
0.504826989 1
< 0.1%
0.50610993 1
< 0.1%
0.507244452 1
< 0.1%
0.508066914 1
< 0.1%
0.509225952 1
< 0.1%
ValueCountFrequency (%)
9.999443297 1
< 0.1%
9.998912209 1
< 0.1%
9.997448266 1
< 0.1%
9.996315009 1
< 0.1%
9.995380178 1
< 0.1%
9.994829527 1
< 0.1%
9.994219334 1
< 0.1%
9.993103849 1
< 0.1%
9.992281173 1
< 0.1%
9.990501326 1
< 0.1%

Power (p1)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.75
Minimum1.5825897
Maximum5.864418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:33.927360image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1.5825897
5-th percentile2.5018464
Q13.2182998
median3.7510254
Q34.2824202
95-th percentile4.9887399
Maximum5.864418
Range4.2818283
Interquartile range (IQR)1.0641204

Descriptive statistics

Standard deviation0.7521601
Coefficient of variation (CV)0.20057603
Kurtosis-0.38946866
Mean3.75
Median Absolute Deviation (MAD)0.53214236
Skewness-0.01269001
Sum37500
Variance0.56574482
MonotonicityNot monotonic
2024-05-05T10:50:34.259497image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.763084772 1
 
< 0.1%
4.770650241 1
 
< 0.1%
4.247526469 1
 
< 0.1%
3.870693105 1
 
< 0.1%
2.843262626 1
 
< 0.1%
2.848058872 1
 
< 0.1%
4.368886789 1
 
< 0.1%
2.416370421 1
 
< 0.1%
3.221192957 1
 
< 0.1%
4.91848394 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1.582589665 1
< 0.1%
1.64285964 1
< 0.1%
1.675633317 1
< 0.1%
1.680221235 1
< 0.1%
1.687356265 1
< 0.1%
1.695575512 1
< 0.1%
1.718919091 1
< 0.1%
1.72283675 1
< 0.1%
1.764910783 1
< 0.1%
1.785255925 1
< 0.1%
ValueCountFrequency (%)
5.86441796 1
< 0.1%
5.827284501 1
< 0.1%
5.818705472 1
< 0.1%
5.814128632 1
< 0.1%
5.798273137 1
< 0.1%
5.784141518 1
< 0.1%
5.752706042 1
< 0.1%
5.750333644 1
< 0.1%
5.733204846 1
< 0.1%
5.732533226 1
< 0.1%

Power (p2)
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2500005
Minimum-1.999891
Maximum-0.50010828
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.3 KiB
2024-05-05T10:50:34.608861image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.999891
5-th percentile-1.9248743
Q1-1.6249015
median-1.2499663
Q3-0.87497709
95-th percentile-0.57513129
Maximum-0.50010828
Range1.4997827
Interquartile range (IQR)0.74992441

Descriptive statistics

Standard deviation0.43303485
Coefficient of variation (CV)-0.34642775
Kurtosis-1.2000016
Mean-1.2500005
Median Absolute Deviation (MAD)0.37500801
Skewness4.1458176 × 10-6
Sum-12500.005
Variance0.18751918
MonotonicityNot monotonic
2024-05-05T10:50:34.959807image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.782603631 1
 
< 0.1%
-1.395206792 1
 
< 0.1%
-0.663566199 1
 
< 0.1%
-1.427060978 1
 
< 0.1%
-0.999536877 1
 
< 0.1%
-1.58264073 1
 
< 0.1%
-1.980608378 1
 
< 0.1%
-1.143506794 1
 
< 0.1%
-1.433561891 1
 
< 0.1%
-1.264557582 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-1.999890956 1
< 0.1%
-1.999741394 1
< 0.1%
-1.999655126 1
< 0.1%
-1.999505657 1
< 0.1%
-1.999370708 1
< 0.1%
-1.999184348 1
< 0.1%
-1.99899519 1
< 0.1%
-1.998857442 1
< 0.1%
-1.998654466 1
< 0.1%
-1.998506587 1
< 0.1%
ValueCountFrequency (%)
-0.500108276 1
< 0.1%
-0.500274063 1
< 0.1%
-0.500314182 1
< 0.1%
-0.500460374 1
< 0.1%
-0.500610409 1
< 0.1%
-0.500780542 1
< 0.1%
-0.50091497 1
< 0.1%
-0.501098496 1
< 0.1%
-0.501236063 1
< 0.1%
-0.501430391 1
< 0.1%

Power (p3)
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2499997
Minimum-1.9999447
Maximum-0.50007225
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.3 KiB
2024-05-05T10:50:35.377088image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.9999447
5-th percentile-1.9248885
Q1-1.6250253
median-1.2499743
Q3-0.87504312
95-th percentile-0.5751059
Maximum-0.50007225
Range1.4998724
Interquartile range (IQR)0.74998218

Descriptive statistics

Standard deviation0.4330351
Coefficient of variation (CV)-0.34642815
Kurtosis-1.2000026
Mean-1.2499997
Median Absolute Deviation (MAD)0.37504695
Skewness4.4593693 × 10-6
Sum-12499.997
Variance0.18751939
MonotonicityNot monotonic
2024-05-05T10:50:35.779095image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.25739483 1
 
< 0.1%
-1.646210985 1
 
< 0.1%
-1.9868494 1
 
< 0.1%
-0.59656876 1
 
< 0.1%
-0.933490317 1
 
< 0.1%
-0.66941681 1
 
< 0.1%
-1.392166116 1
 
< 0.1%
-0.71198502 1
 
< 0.1%
-0.591071435 1
 
< 0.1%
-1.792652683 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-1.999944669 1
< 0.1%
-1.999714735 1
< 0.1%
-1.999607823 1
< 0.1%
-1.999456735 1
< 0.1%
-1.999298915 1
< 0.1%
-1.999159229 1
< 0.1%
-1.999034075 1
< 0.1%
-1.998940506 1
< 0.1%
-1.998720389 1
< 0.1%
-1.998596228 1
< 0.1%
ValueCountFrequency (%)
-0.500072252 1
< 0.1%
-0.500289623 1
< 0.1%
-0.50036054 1
< 0.1%
-0.500575137 1
< 0.1%
-0.500681724 1
< 0.1%
-0.500842791 1
< 0.1%
-0.500918484 1
< 0.1%
-0.501092439 1
< 0.1%
-0.50126592 1
< 0.1%
-0.501448066 1
< 0.1%

Power (p4)
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.2499998
Minimum-1.9999263
Maximum-0.50002453
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.3 KiB
2024-05-05T10:50:36.176565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-1.9999263
5-th percentile-1.924927
Q1-1.6249595
median-1.2500073
Q3-0.87506466
95-th percentile-0.57503839
Maximum-0.50002453
Range1.4999018
Interquartile range (IQR)0.74989487

Descriptive statistics

Standard deviation0.43303499
Coefficient of variation (CV)-0.34642806
Kurtosis-1.1999956
Mean-1.2499998
Median Absolute Deviation (MAD)0.37497515
Skewness9.1575479 × 10-7
Sum-12499.998
Variance0.18751931
MonotonicityNot monotonic
2024-05-05T10:50:36.547258image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.723086311 1
 
< 0.1%
-1.729232465 1
 
< 0.1%
-1.59711087 1
 
< 0.1%
-1.847063367 1
 
< 0.1%
-0.910235432 1
 
< 0.1%
-0.596001332 1
 
< 0.1%
-0.996112294 1
 
< 0.1%
-0.560878607 1
 
< 0.1%
-1.196559631 1
 
< 0.1%
-1.861273675 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
-1.999926332 1
< 0.1%
-1.99978688 1
< 0.1%
-1.999675101 1
< 0.1%
-1.999449197 1
< 0.1%
-1.999335894 1
< 0.1%
-1.99922929 1
< 0.1%
-1.999075617 1
< 0.1%
-1.998910674 1
< 0.1%
-1.998659956 1
< 0.1%
-1.998577431 1
< 0.1%
ValueCountFrequency (%)
-0.500024529 1
< 0.1%
-0.500198283 1
< 0.1%
-0.500425643 1
< 0.1%
-0.500592363 1
< 0.1%
-0.500692252 1
< 0.1%
-0.500811264 1
< 0.1%
-0.500937245 1
< 0.1%
-0.501144092 1
< 0.1%
-0.501214853 1
< 0.1%
-0.50148446 1
< 0.1%

Price Elasticity Coefficient (g1)
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52499979
Minimum0.050009304
Maximum0.99993707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:36.936330image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.050009304
5-th percentile0.097526154
Q10.28752138
median0.52500915
Q30.76243485
95-th percentile0.95249505
Maximum0.99993707
Range0.94992777
Interquartile range (IQR)0.47491348

Descriptive statistics

Standard deviation0.27425553
Coefficient of variation (CV)0.5223917
Kurtosis-1.1999986
Mean0.52499979
Median Absolute Deviation (MAD)0.23748181
Skewness2.9564567 × 10-6
Sum5249.9979
Variance0.075216097
MonotonicityNot monotonic
2024-05-05T10:50:37.284124image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.650456461 1
 
< 0.1%
0.690958302 1
 
< 0.1%
0.812862076 1
 
< 0.1%
0.179230288 1
 
< 0.1%
0.626694163 1
 
< 0.1%
0.648436003 1
 
< 0.1%
0.104044686 1
 
< 0.1%
0.927805676 1
 
< 0.1%
0.217075954 1
 
< 0.1%
0.700086875 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.050009304 1
< 0.1%
0.050184193 1
< 0.1%
0.050211922 1
< 0.1%
0.050324037 1
< 0.1%
0.050389782 1
< 0.1%
0.05052884 1
< 0.1%
0.050591785 1
< 0.1%
0.050727538 1
< 0.1%
0.050824069 1
< 0.1%
0.050888166 1
< 0.1%
ValueCountFrequency (%)
0.999937073 1
< 0.1%
0.999846015 1
< 0.1%
0.999805994 1
< 0.1%
0.999628049 1
< 0.1%
0.999599955 1
< 0.1%
0.999466657 1
< 0.1%
0.999357153 1
< 0.1%
0.999294984 1
< 0.1%
0.999175384 1
< 0.1%
0.999125142 1
< 0.1%

Price Elasticity Coefficient (g2)
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52500026
Minimum0.050053101
Maximum0.99994429
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:37.638587image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.050053101
5-th percentile0.097500525
Q10.28755164
median0.52500315
Q30.76249031
95-th percentile0.95250001
Maximum0.99994429
Range0.94989119
Interquartile range (IQR)0.47493868

Descriptive statistics

Standard deviation0.2742549
Coefficient of variation (CV)0.52239004
Kurtosis-1.2000016
Mean0.52500026
Median Absolute Deviation (MAD)0.23749126
Skewness-6.1377838 × 10-7
Sum5250.0026
Variance0.075215752
MonotonicityNot monotonic
2024-05-05T10:50:37.998736image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.859578106 1
 
< 0.1%
0.528251035 1
 
< 0.1%
0.929569006 1
 
< 0.1%
0.401928134 1
 
< 0.1%
0.11295274 1
 
< 0.1%
0.594929761 1
 
< 0.1%
0.054406473 1
 
< 0.1%
0.805274932 1
 
< 0.1%
0.680618462 1
 
< 0.1%
0.562769372 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.050053101 1
< 0.1%
0.050129783 1
< 0.1%
0.050273639 1
< 0.1%
0.050296364 1
< 0.1%
0.050385647 1
< 0.1%
0.050488645 1
< 0.1%
0.0506418 1
< 0.1%
0.050708349 1
< 0.1%
0.050784307 1
< 0.1%
0.050907937 1
< 0.1%
ValueCountFrequency (%)
0.999944294 1
< 0.1%
0.999893486 1
< 0.1%
0.999739098 1
< 0.1%
0.999696622 1
< 0.1%
0.999568128 1
< 0.1%
0.99948421 1
< 0.1%
0.999341766 1
< 0.1%
0.999309346 1
< 0.1%
0.999203052 1
< 0.1%
0.999068833 1
< 0.1%

Price Elasticity Coefficient (g3)
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52500034
Minimum0.050053692
Maximum0.99998183
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:38.363013image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.050053692
5-th percentile0.097517
Q10.28751364
median0.52501469
Q30.76244022
95-th percentile0.95248568
Maximum0.99998183
Range0.94992814
Interquartile range (IQR)0.47492657

Descriptive statistics

Standard deviation0.27425481
Coefficient of variation (CV)0.52238978
Kurtosis-1.1999935
Mean0.52500034
Median Absolute Deviation (MAD)0.23748279
Skewness3.2043137 × 10-6
Sum5250.0034
Variance0.075215702
MonotonicityNot monotonic
2024-05-05T10:50:38.735801image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.887444921 1
 
< 0.1%
0.148013881 1
 
< 0.1%
0.070145451 1
 
< 0.1%
0.843092215 1
 
< 0.1%
0.051026578 1
 
< 0.1%
0.375026901 1
 
< 0.1%
0.669895413 1
 
< 0.1%
0.837993899 1
 
< 0.1%
0.457294944 1
 
< 0.1%
0.827932676 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.050053692 1
< 0.1%
0.050103888 1
< 0.1%
0.050270427 1
< 0.1%
0.05037667 1
< 0.1%
0.050464846 1
< 0.1%
0.050508591 1
< 0.1%
0.050624738 1
< 0.1%
0.050759085 1
< 0.1%
0.050789457 1
< 0.1%
0.050921353 1
< 0.1%
ValueCountFrequency (%)
0.999981832 1
< 0.1%
0.999839457 1
< 0.1%
0.999744823 1
< 0.1%
0.999653995 1
< 0.1%
0.999581011 1
< 0.1%
0.999436972 1
< 0.1%
0.999336184 1
< 0.1%
0.999279018 1
< 0.1%
0.999171157 1
< 0.1%
0.999126849 1
< 0.1%

Price Elasticity Coefficient (g4)
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52500009
Minimum0.050028494
Maximum0.99993005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-05T10:50:39.163068image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.050028494
5-th percentile0.097585435
Q10.28749444
median0.52500163
Q30.76243299
95-th percentile0.95248802
Maximum0.99993005
Range0.94990155
Interquartile range (IQR)0.47493855

Descriptive statistics

Standard deviation0.27425484
Coefficient of variation (CV)0.52239009
Kurtosis-1.2000028
Mean0.52500009
Median Absolute Deviation (MAD)0.23749669
Skewness3.8710598 × 10-6
Sum5250.0009
Variance0.07521572
MonotonicityNot monotonic
2024-05-05T10:50:39.526834image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.958033988 1
 
< 0.1%
0.354951886 1
 
< 0.1%
0.773975371 1
 
< 0.1%
0.62229072 1
 
< 0.1%
0.886235647 1
 
< 0.1%
0.766276644 1
 
< 0.1%
0.250518815 1
 
< 0.1%
0.594117168 1
 
< 0.1%
0.726298685 1
 
< 0.1%
0.150383852 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.050028494 1
< 0.1%
0.050170232 1
< 0.1%
0.05023109 1
< 0.1%
0.0503357 1
< 0.1%
0.050457947 1
< 0.1%
0.0505464 1
< 0.1%
0.050632864 1
< 0.1%
0.050730921 1
< 0.1%
0.050794743 1
< 0.1%
0.050922752 1
< 0.1%
ValueCountFrequency (%)
0.999930048 1
< 0.1%
0.999881627 1
< 0.1%
0.999738804 1
< 0.1%
0.999640083 1
< 0.1%
0.999575727 1
< 0.1%
0.999455135 1
< 0.1%
0.99940945 1
< 0.1%
0.999328006 1
< 0.1%
0.999185109 1
< 0.1%
0.999078777 1
< 0.1%

Stability
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
unstable
6380 
stable
3620 

Length

Max length8
Median length8
Mean length7.276
Min length6

Characters and Unicode

Total characters72760
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunstable
2nd rowstable
3rd rowunstable
4th rowunstable
5th rowunstable

Common Values

ValueCountFrequency (%)
unstable 6380
63.8%
stable 3620
36.2%

Length

2024-05-05T10:50:39.931473image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-05T10:50:40.307622image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
unstable 6380
63.8%
stable 3620
36.2%

Most occurring characters

ValueCountFrequency (%)
s 10000
13.7%
t 10000
13.7%
a 10000
13.7%
b 10000
13.7%
l 10000
13.7%
e 10000
13.7%
u 6380
8.8%
n 6380
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72760
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 10000
13.7%
t 10000
13.7%
a 10000
13.7%
b 10000
13.7%
l 10000
13.7%
e 10000
13.7%
u 6380
8.8%
n 6380
8.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 72760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 10000
13.7%
t 10000
13.7%
a 10000
13.7%
b 10000
13.7%
l 10000
13.7%
e 10000
13.7%
u 6380
8.8%
n 6380
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 10000
13.7%
t 10000
13.7%
a 10000
13.7%
b 10000
13.7%
l 10000
13.7%
e 10000
13.7%
u 6380
8.8%
n 6380
8.8%

Interactions

2024-05-05T10:50:27.545990image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:01.715819image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:03.936525image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:06.131372image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:08.928150image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:11.092361image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:13.541985image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:15.951855image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:18.245785image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:20.743644image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:22.646988image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:25.491260image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:27.707589image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:01.935789image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:04.103299image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:06.296916image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:09.125691image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:11.240139image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:13.780836image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:16.141057image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:18.421580image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:20.885139image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:22.791919image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:25.677161image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:27.864654image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:02.139158image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:04.251291image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:06.443099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:09.301037image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:11.426413image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:13.959867image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:16.360743image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:18.602043image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:21.036336image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:22.938057image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:25.844406image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:28.033193image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:02.349208image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:04.424140image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:06.617844image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:09.515341image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:11.647359image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:14.169848image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:16.546718image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:18.774625image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:21.179538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:23.091533image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:26.016429image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:28.181993image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:02.539666image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:04.594867image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:06.781469image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:09.689944image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:11.860659image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:14.389789image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:16.752069image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:18.998526image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:21.331952image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:23.282977image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:26.188925image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:28.336716image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:02.692160image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:04.745119image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:06.945064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:09.845945image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:12.066720image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:14.570949image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:16.931732image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:19.194263image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:21.478400image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:23.480920image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:26.343877image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:28.507719image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:02.872705image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:04.933870image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:07.152231image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:10.002097image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:12.267435image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:14.768270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:17.134735image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:19.480321image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:21.659310image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:23.716136image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:26.545578image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:28.701600image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:03.049431image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:05.138142image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:07.364547image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:10.221581image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:12.484660image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:14.965866image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:17.317638image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:19.681657image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:21.868850image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:24.014984image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:26.746350image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:28.878381image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:03.235250image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:05.360087image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:07.597969image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:10.429230image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:12.778846image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:15.181854image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:17.485941image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:19.898081image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:22.030935image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:24.193718image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:26.925210image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:29.059968image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:03.424924image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:05.585318image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:07.809481image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:10.602944image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:12.971566image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:15.378633image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:17.681064image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:20.117523image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:22.177538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:24.433142image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:27.071767image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:29.215917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:03.627996image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:05.779806image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:08.061885image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:10.775088image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:13.180037image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:15.566075image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:17.904001image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:20.355148image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:22.334663image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:24.675386image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:27.228355image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:29.389888image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:03.779747image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:05.955626image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:08.224776image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:10.925495image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:13.368722image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:15.747688image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:18.073047image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:20.566058image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:22.492231image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:24.854558image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2024-05-05T10:50:27.384647image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2024-05-05T10:50:40.608930image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Power (p1)Power (p2)Power (p3)Power (p4)Price Elasticity Coefficient (g1)Price Elasticity Coefficient (g2)Price Elasticity Coefficient (g3)Price Elasticity Coefficient (g4)Reaction Time (tau1)Reaction Time (tau2)Reaction Time (tau3)Reaction Time (tau4)Stability
Power (p1)1.000-0.564-0.576-0.5720.0030.0140.002-0.0170.025-0.0040.019-0.0040.014
Power (p2)-0.5641.0000.002-0.0070.016-0.0180.0080.020-0.0150.007-0.0030.0110.000
Power (p3)-0.5760.0021.0000.013-0.003-0.012-0.006-0.010-0.0160.008-0.0090.0060.006
Power (p4)-0.572-0.0070.0131.000-0.0140.003-0.0040.018-0.016-0.006-0.018-0.0110.026
Price Elasticity Coefficient (g1)0.0030.016-0.003-0.0141.0000.008-0.0060.0120.011-0.002-0.012-0.0040.197
Price Elasticity Coefficient (g2)0.014-0.018-0.0120.0030.0081.000-0.013-0.0150.0150.0150.0080.0080.216
Price Elasticity Coefficient (g3)0.0020.008-0.006-0.004-0.006-0.0131.0000.007-0.0010.0170.0150.0030.229
Price Elasticity Coefficient (g4)-0.0170.020-0.0100.0180.012-0.0150.0071.0000.005-0.012-0.011-0.0000.203
Reaction Time (tau1)0.025-0.015-0.016-0.0160.0110.015-0.0010.0051.0000.016-0.006-0.0170.290
Reaction Time (tau2)-0.0040.0070.008-0.006-0.0020.0150.017-0.0120.0161.0000.014-0.0020.287
Reaction Time (tau3)0.019-0.003-0.009-0.018-0.0120.0080.015-0.011-0.0060.0141.0000.0040.276
Reaction Time (tau4)-0.0040.0110.006-0.011-0.0040.0080.003-0.000-0.017-0.0020.0041.0000.281
Stability0.0140.0000.0060.0260.1970.2160.2290.2030.2900.2870.2760.2811.000

Missing values

2024-05-05T10:50:29.679478image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-05T10:50:30.329032image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Reaction Time (tau1)Reaction Time (tau2)Reaction Time (tau3)Reaction Time (tau4)Power (p1)Power (p2)Power (p3)Power (p4)Price Elasticity Coefficient (g1)Price Elasticity Coefficient (g2)Price Elasticity Coefficient (g3)Price Elasticity Coefficient (g4)Stability
02.9590603.0798858.3810259.7807543.763085-0.782604-1.257395-1.7230860.6504560.8595780.8874450.958034unstable
19.3040974.9025243.0475411.3693575.067812-1.940058-1.872742-1.2550120.4134410.8624140.5621390.781760stable
28.9717078.8484283.0464791.2145183.405158-1.207456-1.277210-0.9204920.1630410.7666890.8394440.109853unstable
30.7164157.6696004.4866412.3405633.963791-1.027473-1.938944-0.9973740.4462090.9767440.9293810.362718unstable
43.1341127.6087724.9437599.8575733.525811-1.125531-1.845975-0.5543050.7971100.4554500.6569470.820923unstable
56.9992099.1092473.7840664.2677884.429669-1.857139-0.670397-1.9021330.2617930.0779300.5428840.469931stable
66.7101663.7652046.9293148.8185622.397419-0.614590-1.208826-0.5740040.1778900.3979770.4020460.376630unstable
76.9535121.3791255.7194007.8703073.224495-0.748998-1.186517-1.2889800.3713850.6332040.7327410.380544unstable
84.6898524.0077471.4785733.7337874.041300-1.410344-1.238204-1.3927510.2697080.2503640.1649410.482439stable
99.8414961.4138229.7698567.6416164.727595-1.991363-0.857637-1.8785940.3763560.5444150.7920390.116263unstable
Reaction Time (tau1)Reaction Time (tau2)Reaction Time (tau3)Reaction Time (tau4)Power (p1)Power (p2)Power (p3)Power (p4)Price Elasticity Coefficient (g1)Price Elasticity Coefficient (g2)Price Elasticity Coefficient (g3)Price Elasticity Coefficient (g4)Stability
99905.7832994.7266141.3402738.6179334.587533-1.950574-1.594137-1.0428220.4458530.6456800.4068640.726180unstable
99910.9989889.9249168.9265632.8859413.660232-1.103521-1.105641-1.4510700.7176600.9549190.4911070.692023unstable
99923.1144424.7810722.4279187.9895092.673156-0.918191-0.652736-1.1022280.8679500.8888580.4605870.965026unstable
99935.7541913.0327435.0848034.6336245.199250-1.717030-1.713212-1.7690090.1572840.9759210.5115550.696591unstable
99942.0429548.5143358.1738095.4666353.783797-1.639912-0.662469-1.4814170.1541290.9444860.0532250.499109unstable
99952.9304069.4876272.3765236.1877973.343416-0.658054-1.449106-1.2362560.6017090.7796420.8135120.608385unstable
99963.3922991.2748272.9549476.8947594.349512-1.663661-0.952437-1.7334140.5020790.5672420.2858800.366120stable
99972.3640342.8420308.7763911.0089064.299976-1.380719-0.943884-1.9753730.4878380.9865050.1492860.145984stable
99989.6315113.9943982.7570717.8213472.514755-0.966330-0.649915-0.8985100.3652460.5875580.8891180.818391unstable
99996.5305276.7817904.3496958.6731383.492807-1.390285-1.532193-0.5703290.0730560.5054410.3787610.942631unstable